Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability

• URL: http://arxiv.org/abs/2509.03547v2
• Date: Fri, 05 Sep 2025 16:42:17 GMT
• Title: Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability
• Authors: Rogério Almeida Gouvêa, Pierre-Paul De Breuck, Tatiane Pretto, Gian-Marco Rignanese, Marcos José Leite Santos,
• Abstract summary: MatterVial is an innovative hybrid framework for feature-based machine learning in materials science.<n>Our approach combines the chemical transparency of traditional feature-based models with the predictive power of deep learning architectures.<n>An integrated interpretability module, employing surrogate models and symbolic regression, decodes the latent GNN-derived descriptors into explicit, physically meaningful formulas.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study introduces MatterVial, an innovative hybrid framework for feature-based machine learning in materials science. MatterVial expands the feature space by integrating latent representations from a diverse suite of pretrained graph neural network (GNN) models including: structure-based (MEGNet), composition-based (ROOST), and equivariant (ORB) graph networks, with computationally efficient, GNN-approximated descriptors and novel features from symbolic regression. Our approach combines the chemical transparency of traditional feature-based models with the predictive power of deep learning architectures. When augmenting the feature-based model MODNet on Matbench tasks, this method yields significant error reductions and elevates its performance to be competitive with, and in several cases superior to, state-of-the-art end-to-end GNNs, with accuracy increases exceeding 40% for multiple tasks. An integrated interpretability module, employing surrogate models and symbolic regression, decodes the latent GNN-derived descriptors into explicit, physically meaningful formulas. This unified framework advances materials informatics by providing a high-performance, transparent tool that aligns with the principles of explainable AI, paving the way for more targeted and autonomous materials discovery.

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